Optimal Graph Search Segmentation Using Arc-weighted Graph for Simultaneous Surface Detection of Bladder and Prostate

We present a novel method for globally optimal surface segmentation of multiple mutually interacting objects, incorporating both edge and shape knowledge in a 3-D graph-theoretic approach. Hard surface interacting constraints are enforced in the interacting regions, preserving the geometric relationship of those partially interacting surfaces. The soft smoothness a priori shape compliance is introduced into the energy functional to provide shape guidance. The globally optimal surfaces can be simultaneously achieved by solving a maximum flow problem based on an arc-weighted graph representation. Representing the segmentation problem in an arc-weighted graph, one can incorporate a wider spectrum of constraints into the formulation, thus increasing segmentation accuracy and robustness in volumetric image data. To the best of our knowledge, our method is the first attempt to introduce the arc-weighted graph representation into the graph-searching approach for simultaneous segmentation of...

• Type: Conference
• Year: 2009
• Where: MICCAI
• Authors: Qi Song, Xiaodong Wu, Yunlong Liu, Mark Smith, John Buatti, and Milan Sonka
• Keywords: Bladder, CT, Graph Search, MICCAI 2009, Prostate, Segmentation